CITATION.cff

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title: 'Knowledge Enhanced Graph Neural Networks '
message: 'If you use this software, please cite it as below.'
type: software
authors:
  - given-names: Luisa
    family-names: Werner
    affiliation: INRIA Grenoble
    email: luisa.werner@inria.fr
    orcid: 'https://orcid.org/0000-0002-1431-6490'
repository-code: 'https://github.com/LuisaWerner/kegnn'
abstract: >-
  Graph data is omnipresent and has a wide variety of
  applications, such as in natural science, social networks,
  or the semantic web. However, while being rich in
  information, graphs are often noisy and incomplete. As a
  result, graph completion tasks, such as node
  classification or link prediction, have gained attention.
  On one hand, neural methods, such as graph neural
  networks, have proven to be robust tools for learning rich
  representations of noisy graphs. On the other hand,
  symbolic methods enable exact reasoning on graphs. We
  propose Knowledge Enhanced Graph Neural Networks (KeGNN),
  a neurosymbolic framework for graph completion that
  combines both paradigms as it allows for the integration
  of prior knowledge into a graph neural network model.
  Essentially, KeGNN consists of a graph neural network as a
  base upon which knowledge enhancement layers are stacked
  with the goal of refining predictions with respect to
  prior knowledge. We instantiate KeGNN in conjunction with
  two state of the art graph neural networks, Graph
  Convolutional Networks and Graph Attention Networks, and
  evaluate KeGNN on multiple benchmark datasets for node
  classification.
keywords:
  - Graph Neural Networks
  - Neuro-Symbolic Integration
  - Knowledge Graphs
license: MIT
commit: 7a04139